It is well known that, in general, a deterministic formulation of the afore-mentioned inverse problem can be ill-posed. Although different regularization procedures have been developed over the last few decades to deal with this ill-posedness, it is now well-established that a statistical formulation of the inverse problem, based on a Bayesian framework, is very suitable in this context [1]}, [2]}, [3]}.
